scholarly journals Adaptive Non-Negative Geographically Weighted Regression for Population Density Estimation Based on Nighttime Light

2019 ◽  
Vol 8 (1) ◽  
pp. 26 ◽  
Author(s):  
Hone-Jay Chu ◽  
Chen-Han Yang ◽  
Chelsea Chou
Keyword(s):  
Population Density ◽  
Geographically Weighted Regression ◽  
Rural Population ◽  
Goodness Of Fit ◽  
Strong Relationship ◽  
Weighted Regression ◽  
Western Boundary ◽  
Nighttime Lights ◽  
Nighttime Light ◽  
Nighttime Light Imagery

Nighttime light imagery provides a perspective for studying urbanization and socioeconomic changes. Traditional global regression models have been applied to explore the nonspatial relationship between nighttime lights and population density. In this study, geographically weighted regression (GWR) identifies the spatially varying relationships between population density and nighttime lights in mainland China. However, the rural population does not have a strong relationship with remote-sensing spectral features. The rural population estimation using nighttime light data alone easily identifies meaningless negative population density in the rural area. This study proposes an adaptive non-negative GWR (ANNGWR) to explore the spatial pattern of population density by using nonnegative constraints with an adaptive bandwidth of kernel. The ANNGWR solves the negative value of population density and serious overestimation of the western boundary. The result shows that the ANNGWR provides the best goodness-of-fit compared with linear regression and original GWR. This study applies Moran’s I index to prove that the ANNGWR substantially decreases the spatial autocorrelation of the model residual. The model offers a robust and effective approach for estimating the spatial patterns of regional population density solely on the basis of nighttime light imagery.

scholarly journals Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic

2009 ◽  
Vol 67 (1) ◽  
pp. 145-154 ◽  
Author(s):  
Matthew J. S. Windle ◽  
George A. Rose ◽  
Rodolphe Devillers ◽  
Marie-Josée Fortin
Keyword(s):  
Survey Data ◽  
Geographically Weighted Regression ◽  
Goodness Of Fit ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Weighted Regression ◽  
Snow Crab ◽  
Implicit Assumption ◽  
Northwest Atlantic ◽  
Goodness Of Fit Tests

Abstract Windle, M. J. S., Rose, G. A., Devillers, R., and Fortin, M-J. 2010. Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic. – ICES Journal of Marine Science, 67: 145–154. Analyses of fisheries data have traditionally been performed under the implicit assumption that ecological relationships do not vary within management areas (i.e. assuming spatially stationary processes). We question this assumption using a local modelling technique, geographically weighted regression (GWR), not previously used in fisheries analyses. Outputs of GWR are compared with those of global logistic regression and generalized additive models (GAMs) in predicting the distribution of northern cod off Newfoundland, Canada, based on environmental (temperature and distance from shore) and biological factors (snow crab and northern shrimp) from 2001. Results from the GWR models explained significantly more variability than the global logistic and GAM regressions, as shown by goodness-of-fit tests and a reduction in the spatial autocorrelation of model residuals. GWR results revealed spatial regions in the relationships between cod and explanatory variables and that the significance and direction of these relationships varied locally. A k-means cluster analysis based on GWR t-values was used to delineate distinct zones of species–environment relationships. The advantages and limitations of GWR are discussed in terms of potential application to fisheries ecology.

scholarly journals PEMODELAN INCIDENT RATE DEMAM BERDARAH DENGUE DI INDONESIA YANG BERKAITAN DENGAN FAKTOR LINGKUNGAN MENGGUNAKAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR)

EKOLOGIA ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 64-73
Author(s):  
Kiki Amelia ◽  
Latifa Oktafiani Asril ◽  
Lasmi Febrianti
Keyword(s):  
Population Density ◽  
Dengue Hemorrhagic Fever ◽  
Geographically Weighted Regression ◽  
Physical Environment ◽  
Secondary Data ◽  
Hemorrhagic Fever ◽  
Least Square ◽  
Gaussian Kernel ◽  
Weighted Regression ◽  
Incident Rate

Dengue hemorrhagic fever cases in Indonesia often occur in cities and villages. Every year hundreds to thousands of people must be hospitalized due to this disease. There are several factors of the physical environment that directly or indirectly influence the transmission of this disease. Such as rainfall, air temperature, and humidity. In addition to the physical environment there are several other factors that can increase the occurrence of dengue cases, namely population density and the level of larvae free in an area. For this reason, we conducted a study of the above factors and their contribution in the addition of dengue cases that occurred in Indonesia in 2015 using secondary data. The purpose of this study is to identify and make a BDB iricident rate model related to environmental factors such as temperature, humidity, population density, and the amount of rainfall on the number of cases of dengue hemorrhagic fever in Indonesia in 2015. The method used is the Geographically Weighted Regression method. (GWR). In the GWR model the parameter estimation uses Weighted Least Square (WLS) by weighting the gaussian kernel function. The results of the study concluded that modeling with GWR was better than linear regression and the variables were significantly different in each region.

scholarly journals PEMODELAN SEMIPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION PADA KASUS PNEUMONIA BALITA PROVINSI JAWA TENGAH

2021 ◽  
Vol 10 (2) ◽  
pp. 250-258
Author(s):  
Putri Fajar Utami ◽  
Agus Rusgiyono ◽  
Dwi Ispriyanti
Keyword(s):  
Population Density ◽  
Akaike Information Criterion ◽  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Weighted Regression ◽  
Severe Malnutrition ◽  
Average Temperature ◽  
Central Java ◽  
Non Local ◽  
Local Parameters

Geographical and inter-regional differences have contributed to the diversity of child pneumonia cases in Central Java, so  a spatial regression modelling is formed that is called Geographically Weighted Regression (GWR). GWR is a development of linear regression by involving diverse factors geographical location, so that local parameters are produced.  Sometimes, there are non-local GWR parameters. To overcome some non-local parameters, Semiparametric Geographically Weighted Regression (SGWR) is formed to develop a GWR model with local and global influences simultaneously. SGWR Model is used to estimate the model of percentage of children with pneumonia in Central Java with population density, average temperature, percentage of children with severe malnutrition, percentage of children with under the red line weight, percentage of households behave in clean and healthy lives, and percentage of children who measles immunized. SGWR models on percentage of children with pneumonia in Central Java produce locally significant variables that is population density, average temperature, and percentage of households behave in clean and healthy lives. Variable that globally significant is percentage of children with severe malnutrition. Based on Akaike Information Criterion (AIC), SGWR is a better model to analize percentage of children with pneumonia in Central Java because of smallest AIC. Keywords: Akaike Information Criterion, Geographically Weighted Regression, Semiparametric Geographically Weighted Regression

scholarly journals Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model

2020 ◽  
Vol 12 (12) ◽  
pp. 5018
Author(s):  
Yanyan Chen ◽  
Hanqiang Qian ◽  
Yang Wang
Keyword(s):  
Land Use ◽  
Population Density ◽  
Regression Model ◽  
Mobile Phone ◽  
Geographically Weighted Regression ◽  
Mobile Phone Data ◽  
Weighted Regression ◽  
Working Population ◽  
Development Status ◽  
Geographically Weighted Regression Model

Evaluation of urban planning and development is becoming more and more important due to the large-scale urbanization of the world. With the application of mobile phone data, people can analyze the development status of cities from more perspectives. By using the mobile phone data of Beijing, the working population density in different regions was identified. Taking the working population density in Beijing as the research object and combining the land use of the city, the development status of Beijing was evaluated. A geographically weighted regression model (GWR) was used to analyze the difference in the impact of land use on the working population between different regions. By establishing a correlation model between the working population and land use, not only can the city’s development status be evaluated, but it can also help city managers and planners to make decisions to promote better development of Beijing.

scholarly journals The Impact of Built Environment Characteristics on Energy Consumption Using Geographically Weighted Regression in Mashhad, Iran

2017 ◽  
Vol 10 (5) ◽  
pp. 198
Author(s):  
Bita Rezaeian ◽  
Mohammad Rahim Rahnama ◽  
Jafar Javan ◽  
Omid Ali Kharazmi
Keyword(s):  
Energy Consumption ◽  
Population Density ◽  
Fuel Consumption ◽  
Geographically Weighted Regression ◽  
Travel Behavior ◽  
Block Size ◽  
Positive Association ◽  
Weighted Regression ◽  
Main Element ◽  
The Impact

Concerns over rising fuel consumption have prompted research into the influences of built environments on travel behavior. On the basis of data from origin-destination(OD) travel survey data of Mashhad (74287 trip data in 2011) and using Geographically Weighted Regression, socio-demographic characteristics, are shown to be strongly and positively associated with the fuel consumption per capita (car ownership elasticity=0.347878); we also found a positive association between distance to center and designs that are not pedestrian friendly with fuel consumption (average block size=0.147489, distance to center =0.334953) Although the study demonstrates a moderately strong negative elasticity between population density and the fuel consumption(population density = -0.259335). It suggests that the largest energy consumption reductions would come from creating compact communities which have land-use diversity and more walkable areas with pedestrian cycling infrastructure around all of the stations along transit lines.In order to enhance a sustainable urban plan, the socio-economic driving factors should be considered as one of the main element of energy consumption as well.

scholarly journals Selecting Prices Determinants and Including Spatial Effects in Peer-to-Peer Accommodation

2020 ◽  
Vol 9 (4) ◽  
pp. 259 ◽  
Author(s):  
Rafael Suárez-Vega ◽  
Juan M. Hernández
Keyword(s):  
Geographically Weighted Regression ◽  
Goodness Of Fit ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Peer To Peer ◽  
Weighted Regression ◽  
Spatial Effects ◽  
Digital Platforms ◽  
Starting Point ◽  
Wide Range

Peer-to-peer accommodation has grown significantly during the last decades, supported, in part, by digital platforms. These websites make available a wide range of information intended to help the customers’ decision. All these factors, in addition to the property location, may therefore influence rental price. This paper proposes different procedures for an efficient selection of a high number of price determinants in peer-to-peer accommodation when applying the perspective of the geographically weighted regression. As a case study, these procedures have been used to find the factors affecting the rental price of properties advertised on Airbnb in Gran Canaria (Spain). The results show that geographically weighted regression obtains better indicators of goodness of fit than the traditional ordinary least squares method, making it possible to identify those attributes influencing price and how their effect varies according to property locations. Moreover, the results also show that the selection procedures working directly on geographically weighted regression obtain better results than those that take good global solutions as their starting point.

Opting Out in the Empire State: A Geographic Analysis of Opting Out in New York, Spring 2015 & 2016

2020 ◽  
Vol 122 (2) ◽  
pp. 1-24
Author(s):  
Kathryn P. Chapman ◽  
Lydia Ross ◽  
Sherman Dorn
Keyword(s):  
New York ◽  
Population Density ◽  
Geographically Weighted Regression ◽  
Child Poverty ◽  
New York State ◽  
District Level ◽  
Weighted Regression ◽  
York State ◽  
Opting Out ◽  
Opt Out

Background Recently, states have experienced widely varying participation in annual assessments, with the opt-out movement concentrated in New York State and Colorado. Geographic variation between and within states suggests that the diffusion of opting out is multilayered and an appropriate phenomenon to explore geographic dimensions of social movements in education. Purpose The study analyzes the geographic patterns of opting out from state assessments in school districts in New York State. Research Design We conducted linear regression and geographically weighted regression on district-level proportions of third- through eighth-grade students in local public school districts for 2015 and 2016 (n = 623), excluding New York City and charter schools. Independent variables included the district-level proportion of students with disabilities, identified as English Language Learners, and identified as White; census-based small-area child poverty estimates for the districts; and the geographic population density of the district. Linear regressions excluded racial and ethnic dummy variables to reduce collinearity problems, and geographically weighted regression limited geographically varying coefficients to child poverty and population density based on preliminary analyses. Findings The unweighted ordinary least squares (OLS) of district-level opting out in both spring 2015 and spring 2016 are weakly predictive as a whole (adjusted R2 < .20). In both years, population density was a statistically significant but low-magnitude predictor of change in opt-out behavior using OLS. The proportion of students with Individualized Education Plans was positively associated with opt-out behavior, and district-level child poverty was negatively associated with opt-out behavior. The proportion of White students was a statistically significant positive predictor of opt-out behavior in spring 2015 but not statistically significant for 2016, though with a coefficient in the same direction (positive). Analyzing the same data with geographically weighted regression more than doubled the adjusted R2 for each year and demonstrated that there were areas of New York State where the coefficients associated with child poverty and population density reversed direction, with suburban Long Island and the western upstate region as areas with a magnified negative association between district-level child poverty and opting-out percentages. Conclusions In the past five years, social networks have enabled the long-distance organizing of social and political movements in education, including opting-out and teacher walkouts. However, the long-distance transmission of ideas does not explain intrastate variations. In this study, geographically weighted regression revealed the local variations in relationships between opting-out and two key variables. Local networks still matter critically to social organizing around education.

scholarly journals Spatial distribution of rural population using mixed geographically weighted regression: Evidence from Jiangxi Province in China

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250399
Author(s):  
Liguo Zhang ◽  
Langping Leng ◽  
Yongming Zeng ◽  
Xi Lin ◽  
Su Chen
Keyword(s):  
Spatial Distribution ◽  
Geographically Weighted Regression ◽  
Rural Population ◽  
Weighted Regression ◽  
Jiangxi Province ◽  
Influence Coefficient ◽  
Climate Factors ◽  
Mountain Areas ◽  
Negative Effect ◽  
Positive Effect

On the basis of the spatial panel data of 2000, 2005, 2010, and 2015, this study uses a mixed geographically weighted regression model to explore the spatial distribution characteristics and influencing factors of the rural (permanent) population in Jiangxi Province, China. Results show that residents in the county area have a significant spatial positive autocorrelation, especially in the lake and mountain areas and the global Moran’ I index is more than 0.05. The influence of social and economic factors presents spatial homogeneity. The effect of urbanization and per capita disposable income is negative, whereas that of agricultural output value and rural electricity consumption is positive. The influence of climate factors presents spatial heterogeneity. The influence coefficient of rainfall in 2015 ranges from [-0.061, 0.133], which has a negative effect on the southwest mountain areas and a positive effect on the northeast lake areas., The influence coefficient of temperature in 2015 ranges from [-0.110, 0.094], which has a positive effect on the southwest mountain areas and a negative effect on the northeast lake areas. The influence coefficients of wind speed and relative humidity range from [-0.090, 0.153] and [-0.069, 0.130] in 2015 respectively, which further reinforce this effect. Therefore, scholars should pay attention to the universal adaptability of economic and social factors. Moreover, they should consider the spatial difference of climatic factors to promote urbanization following the local conditions. Finally, policymakers and concerned non-governmental institutions should fully understand the sensitivity of the rural population in underdeveloped mountain areas to climate factors to promote their rational distribution.

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